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Multitask Bayesian Federated Learning for Privacy-Preserving English Translation Model Training Across Edge Devices

Domaine:

natural language processing

Type de record:

paper
Créateur:
Yao
Éditeur:
fig
Hôte:avatar
To address core challenges such as resource constraints on edge devices, data privacy concerns, and poor translation quality for low-resource languages, this paper proposes a Multitask Bayesian Federated Learning (MT-BayesFL) framework to achieve efficient, robust, and trustworthy multilingual translation while preserving data locality. The framework's core is multi-task collaboration. Through a lightweight shared encoder and task-specific decoder architecture, the framework enables the natural transfer of general semantic knowledge learned in high-resource languages to low-resource languages via the shared encoder, directly alleviating the data sparsity problem and achieving mutual benefit between tasks.

Visit

doi.orgfigshare.com

Tasks

machine translationtransfer learning

Tags

Translation and interpretation studiesApplications in social sciences and educationArtificial intelligence not elsewhere classified

Licenses

Creative Commons Attribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/legalcode

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